Please complete all required fields!
Conclusion: Machine learning operations (MLOps) adapts principles, practices and measures from developer operations (DevOps), but significantly transforms some aspects to address the different skill sets and quality control challenges and deployment nuances of machine learning (ML) and data engineering.
Implementing MLOps has several benefits, from easing collaboration among project team members to reducing bias in the resulting artificial intelligence (AI) models.
Read more ...
Conclusion: While the current artificial intelligence (AI) initiatives are data-driven, there are instances whereby the current data is insufficient to predict the future. For example, answering the following questions might be challenging if the available data is only of a historical nature irrelevant for forecasting purposes:
The purpose of this note is to provide a framework that can be used to derive sales principles to answer the above questions. The same approach can be used to derive other business processes principles such as procurement, customer service and client complaints tracking.
"Acknowledging the limits of machine learning during AI-enabled transformation" IBRS, 2019-01-06 22:29:52
"Analytics artificial intelligence maturity model" IBRS, 2018-12-03 09:44:43
"Machine learning will displace “extract, transform and load” in business intelligence and data integration" IBRS, 2018-02-01 10:03:37
Conclusion: Artificial intelligence technologies are available in various places such as robotic process automation (RPA), virtual agents and analytics. The purpose of this paper is to provide an AI maturity model in the analytics space. The proposed maturity model can be applied to any type of industry. It provides a roadmap to help improve business performance in the following areas:
Many IT organisations are trying to change their perceived image from high-cost / low quality to value-added service providers. However, many of the adopted approaches revolve around improving just few processes (e.g. problem management). While these processes are important, they are insufficient to produce the desired effect for IT groups to deliver value-added services.
In this IBRS Master Advisory Presentation (MAP), IBRS outlines the high-level issues, surrounding Running IT as a Service from both business and technology viewpoints.This MAP is designed to guide and stimulate discussions between business and technology groups and point the way for more detailed activity. It also provides links to further reading to support these follow-up activities.
The MAP is provided as a set of presentation slides, and as a script and executive briefing document.
Login to read your premium content.